The use of generalised additive models (GAM) in dentistry.
BACKGROUND
Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model.


OBJECTIVE
During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping').


METHODS
After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described.


CONCLUSIONS
GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.
Helfenstein
U
U
Department of Biostatistics, University of Zurich, Switzerland.
Steiner
M
M
Menghini
G
G
D000367:Age Factors; D000465:Algorithms; D000704:Analysis of Variance; D002648:Child; D016001:Confidence Intervals; D004245:DMF Index; D003731:Dental Caries / Q000209:etiology / Q000382:microbiology; D003813:Dentistry / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010512:Periodontal Index; D015995:Prevalence; D011336:Probability; D011379:Prognosis; D012044:Regression Analysis; D012307:Risk Factors; D013295:Streptococcus mutans / Q000502:physiology; D014094:Tooth, Deciduous